Add model card and metadata
Browse filesHi! I'm Niels from the Hugging Face community science team.
This PR adds a comprehensive model card to the repository. It includes:
- Metadata for `license`, `library_name`, and `pipeline_tag`.
- Links to the paper ([Scaling Text-to-Image Diffusion Transformers with Representation Autoencoders](https://huggingface.co/papers/2601.16208)), project page, and official GitHub repository.
- A brief description of the model and its findings.
- Sample CLI usage for inference as found in the official documentation.
- Citation information.
This update will make the model more discoverable and provide users with the necessary context and links to the official implementation.
README.md
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---
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license: mit
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library_name: transformers
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pipeline_tag: image-to-image
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---
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# Scaling Text-to-Image Diffusion Transformers with Representation Autoencoders (Scale-RAE)
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This repository contains artifacts related to the paper [Scaling Text-to-Image Diffusion Transformers with Representation Autoencoders](https://huggingface.co/papers/2601.16208).
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- **Project Page:** [https://rae-dit.github.io/scale-rae/](https://rae-dit.github.io/scale-rae/)
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- **GitHub Repository:** [https://github.com/ZitengWangNYU/Scale-RAE](https://github.com/ZitengWangNYU/Scale-RAE)
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## Introduction
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Representation Autoencoders (RAEs) provide a simplified and powerful alternative to VAEs for large-scale text-to-image generation. Scale-RAE demonstrates that training diffusion models in high-dimensional semantic latent spaces (using encoders like SigLIP-2) leads to faster convergence, better generation quality, and improved stability compared to state-of-the-art VAE-based foundations.
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## Usage
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For detailed instructions on installation, training, and inference, please visit the [official GitHub repository](https://github.com/ZitengWangNYU/Scale-RAE).
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The implementation supports GPU inference and TPU training. To generate images with pre-trained models:
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```bash
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python cli.py t2i --prompt "Can you generate a photo of a cat on a windowsill?"
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```
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## Citation
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```bibtex
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@article{scale-rae-2026,
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title={Scaling Text-to-Image Diffusion Transformers with Representation Autoencoders},
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author={Shengbang Tong and Boyang Zheng and Ziteng Wang and Bingda Tang and Nanye Ma and Ellis Brown and Jihan Yang and Rob Fergus and Yann LeCun and Saining Xie},
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journal={arXiv preprint arXiv:2601.16208},
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year={2026}
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}
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```
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